{
 "cells": [
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "# 自主编程实现logistic回归",
   "id": "342b8cf652c547dd"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-12T13:15:56.279251Z",
     "start_time": "2025-06-12T13:15:56.197850Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import numpy as np\n",
    "import time\n",
    "from sklearn.datasets import fetch_openml\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix, classification_report\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns  # 确保正确导入并设置别名 sns"
   ],
   "id": "73ca7c8702ff44db",
   "outputs": [],
   "execution_count": 11
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-12T13:16:24.858133Z",
     "start_time": "2025-06-12T13:15:56.287257Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 加载MNIST数据集\n",
    "mnist = fetch_openml('mnist_784', version=1)\n",
    "X, y = mnist.data, mnist.target.astype(np.int8)"
   ],
   "id": "ed18c44f6b068f0",
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\PythonProject\\Anaconda3-2024\\envs\\test\\lib\\site-packages\\sklearn\\datasets\\_openml.py:1022: FutureWarning: The default value of `parser` will change from `'liac-arff'` to `'auto'` in 1.4. You can set `parser='auto'` to silence this warning. Therefore, an `ImportError` will be raised from 1.4 if the dataset is dense and pandas is not installed. Note that the pandas parser may return different data types. See the Notes Section in fetch_openml's API doc for details.\n",
      "  warn(\n"
     ]
    }
   ],
   "execution_count": 12
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-12T13:16:25.902144Z",
     "start_time": "2025-06-12T13:16:24.937739Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 数据标准化\n",
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(X)"
   ],
   "id": "a2ebc5a0adb25597",
   "outputs": [],
   "execution_count": 13
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-12T13:16:27.135252Z",
     "start_time": "2025-06-12T13:16:25.967030Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 划分训练集和测试集\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)"
   ],
   "id": "a7a62be2050df3ec",
   "outputs": [],
   "execution_count": 14
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-12T13:16:27.212260Z",
     "start_time": "2025-06-12T13:16:27.199251Z"
    }
   },
   "cell_type": "code",
   "source": [
    "class MyLogisticRegression:\n",
    "    def __init__(self, learning_rate=0.01, epochs=100, num_classes=10):\n",
    "        self.learning_rate = learning_rate\n",
    "        self.epochs = epochs\n",
    "        self.num_classes = num_classes\n",
    "        self.weights = None\n",
    "        self.bias = None\n",
    "\n",
    "    def softmax(self, z):\n",
    "        exp_z = np.exp(z - np.max(z, axis=1, keepdims=True))\n",
    "        return exp_z / np.sum(exp_z, axis=1, keepdims=True)\n",
    "\n",
    "    def fit(self, X, y):\n",
    "        m, n = X.shape\n",
    "        self.weights = np.zeros((n, self.num_classes))\n",
    "        self.bias = np.zeros((1, self.num_classes))\n",
    "\n",
    "        # 将标签转换为独热编码\n",
    "        y_one_hot = np.eye(self.num_classes)[y]\n",
    "\n",
    "        for epoch in range(self.epochs):\n",
    "            z = np.dot(X, self.weights) + self.bias\n",
    "            predictions = self.softmax(z)\n",
    "            dw = (1 / m) * np.dot(X.T, (predictions - y_one_hot))\n",
    "            db = (1 / m) * np.sum(predictions - y_one_hot, axis=0, keepdims=True)\n",
    "            self.weights -= self.learning_rate * dw\n",
    "            self.bias -= self.learning_rate * db\n",
    "\n",
    "    def predict(self, X):\n",
    "        z = np.dot(X, self.weights) + self.bias\n",
    "        predictions = self.softmax(z)\n",
    "        return np.argmax(predictions, axis=1)"
   ],
   "id": "e68b80384317d6ad",
   "outputs": [],
   "execution_count": 15
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-12T13:16:31.511018Z",
     "start_time": "2025-06-12T13:16:27.276252Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 使用自定义实现的Logistic回归\n",
    "my_lr = MyLogisticRegression(learning_rate=0.01, epochs=100, num_classes=10)  # 减少epochs以加快训练速度\n",
    "start_time = time.time()\n",
    "my_lr.fit(X_train, y_train)\n",
    "my_train_time = time.time() - start_time\n",
    "my_pred = my_lr.predict(X_test)"
   ],
   "id": "fa2b0ceaddb613c8",
   "outputs": [],
   "execution_count": 16
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-12T13:16:31.607034Z",
     "start_time": "2025-06-12T13:16:31.561017Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 计算评估指标\n",
    "accuracy = accuracy_score(y_test, my_pred)\n",
    "precision = precision_score(y_test, my_pred, average='weighted')\n",
    "recall = recall_score(y_test, my_pred, average='weighted')\n",
    "f1 = f1_score(y_test, my_pred, average='weighted')\n",
    "conf_matrix = confusion_matrix(y_test, my_pred)\n",
    "class_report = classification_report(y_test, my_pred, digits=4)"
   ],
   "id": "7674578e4ba38c07",
   "outputs": [],
   "execution_count": 17
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-12T13:16:31.670644Z",
     "start_time": "2025-06-12T13:16:31.655627Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 打印评估指标，保留四位小数\n",
    "print(\"My Logistic Regression - Accuracy: {:.4f}\".format(accuracy))\n",
    "print(\"My Logistic Regression - Precision: {:.4f}\".format(precision))\n",
    "print(\"My Logistic Regression - Recall: {:.4f}\".format(recall))\n",
    "print(\"My Logistic Regression - F1-score: {:.4f}\".format(f1))\n",
    "print(\"My Logistic Regression - Confusion Matrix:\\n\", conf_matrix)\n",
    "print(\"My Logistic Regression - Classification Report:\\n\", class_report)\n",
    "print(\"My Logistic Regression - Training Time: {:.4f} seconds\".format(my_train_time))"
   ],
   "id": "94c22319254ed5dd",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "My Logistic Regression - Accuracy: 0.8513\n",
      "My Logistic Regression - Precision: 0.8567\n",
      "My Logistic Regression - Recall: 0.8513\n",
      "My Logistic Regression - F1-score: 0.8498\n",
      "My Logistic Regression - Confusion Matrix:\n",
      " [[1262    1   10    4    0   14   38    5    9    0]\n",
      " [   0 1568    4    6    2    5    3    4    7    1]\n",
      " [  18   72 1123   35   25    2   39   20   40    6]\n",
      " [  12   50   46 1198    2   18   19   35   32   21]\n",
      " [   5   28    9    2 1142    6   15    5    6   77]\n",
      " [  30   90   12  127   32  878   33   14   25   32]\n",
      " [  22   42   14    1   20   19 1275    0    3    0]\n",
      " [   9   66    8    5   21    1    2 1338    1   52]\n",
      " [  23  141   18   78   20   36   14   17  968   42]\n",
      " [  20   47    5   17   67    3    0   86    9 1166]]\n",
      "My Logistic Regression - Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0     0.9008    0.9397    0.9198      1343\n",
      "           1     0.7449    0.9800    0.8464      1600\n",
      "           2     0.8991    0.8138    0.8543      1380\n",
      "           3     0.8133    0.8360    0.8245      1433\n",
      "           4     0.8580    0.8819    0.8698      1295\n",
      "           5     0.8941    0.6897    0.7787      1273\n",
      "           6     0.8866    0.9133    0.8998      1396\n",
      "           7     0.8780    0.8902    0.8840      1503\n",
      "           8     0.8800    0.7133    0.7880      1357\n",
      "           9     0.8346    0.8211    0.8278      1420\n",
      "\n",
      "    accuracy                         0.8513     14000\n",
      "   macro avg     0.8589    0.8479    0.8493     14000\n",
      "weighted avg     0.8567    0.8513    0.8498     14000\n",
      "\n",
      "My Logistic Regression - Training Time: 4.2159 seconds\n"
     ]
    }
   ],
   "execution_count": 18
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-12T13:16:32.027243Z",
     "start_time": "2025-06-12T13:16:31.720629Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 可视化混淆矩阵\n",
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(10, 8))\n",
    "sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues', xticklabels=np.arange(10), yticklabels=np.arange(10))\n",
    "plt.title(\"My Logistic Regression - Confusion Matrix\", fontsize=16)\n",
    "plt.xlabel(\"Predicted Label\", fontsize=14)\n",
    "plt.ylabel(\"True Label\", fontsize=14)\n",
    "plt.show()"
   ],
   "id": "2d6e229990b14037",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 19
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-12T13:16:32.243759Z",
     "start_time": "2025-06-12T13:16:32.074228Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "metrics_df = pd.DataFrame({\n",
    "       'Model': ['my logistic'],\n",
    "       'Accuracy': [0.8513],\n",
    "       'Precision': [0.8567],\n",
    "       'Recall': [0.8513],\n",
    "       'F1': [0.8498]\n",
    "   }).set_index('Model')\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "ax = sns.heatmap(metrics_df, \n",
    "                    annot=True, \n",
    "                    fmt=\".2%\",\n",
    "                    cmap=\"YlGnBu\", \n",
    "                    linewidths=.5,\n",
    "                    cbar_kws={'label': 'Score Scale'},\n",
    "                    annot_kws={\"size\": 12})\n",
    "\n",
    "plt.title(\"Model Performance Comparison\", fontsize=14, pad=20)\n",
    "plt.xticks(rotation=45, ha='right', fontsize=12)\n",
    "plt.yticks(rotation=0, fontsize=12)\n",
    "ax.xaxis.set_label_position('top') \n",
    "ax.xaxis.tick_top()\n",
    "cbar = ax.collections[0].colorbar\n",
    "cbar.set_label('Score (0-1 scale)', rotation=270, labelpad=20)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "   "
   ],
   "id": "7bbf5923954a1ce7",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x600 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 20
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
